





'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 		lowerCamelCase	(   _A   , unittest.TestCase							):
	snake_case_   = KandinskyImgaImgPipeline
	snake_case_   = ["prompt", "image_embeds", "negative_image_embeds", "image"]
	snake_case_   = [
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	]
	snake_case_   = [
	    "generator",
	    "height",
	    "width",
	    "strength",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	snake_case_   = False



	@property
	def 	_lowerCamelCase     ( self						):
			return 32



	@property
	def 	_lowerCamelCase     ( self						):
			return 32



	@property
	def 	_lowerCamelCase     ( self						):
			return self.time_input_dim



	@property
	def 	_lowerCamelCase     ( self						):
			return self.time_input_dim * 4



	@property
	def 	_lowerCamelCase     ( self						):
			return 100



	@property
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"						)
			return tokenizer



	@property
	def 	_lowerCamelCase     ( self						):
			torch.manual_seed(0						)
			lowerCAmelCase  :     Optional[int]       						= MCLIPConfig(
			    numDims=self.cross_attention_dim		,   transformerDimensions=self.text_embedder_hidden_size		,   hidden_size=self.text_embedder_hidden_size		,   intermediate_size=37		,   num_attention_heads=4		,   num_hidden_layers=5		,   vocab_size=1_005		,   )

			lowerCAmelCase  :     Dict       						= MultilingualCLIP(a_						)
			lowerCAmelCase  :     List[Any]       						= text_encoder.eval()

			return text_encoder



	@property
	def 	_lowerCamelCase     ( self						):
			torch.manual_seed(0						)

			lowerCAmelCase  :     List[Any]       						= {
			    "in_channels": 4,
			    # Out channels is double in channels because predicts mean and variance
			    "out_channels": 8,
			    "addition_embed_type": "text_image",
			    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
			    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
			    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
			    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
			    "layers_per_block": 1,
			    "encoder_hid_dim": self.text_embedder_hidden_size,
			    "encoder_hid_dim_type": "text_image_proj",
			    "cross_attention_dim": self.cross_attention_dim,
			    "attention_head_dim": 4,
			    "resnet_time_scale_shift": "scale_shift",
			    "class_embed_type": None,
			}

			lowerCAmelCase  :     Union[str, Any]       						= UNetaDConditionModel(**a_						)
			return model



	@property
	def 	_lowerCamelCase     ( self						):
			return {
			    "block_out_channels": [32, 64],
			    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
			    "in_channels": 3,
			    "latent_channels": 4,
			    "layers_per_block": 1,
			    "norm_num_groups": 8,
			    "norm_type": "spatial",
			    "num_vq_embeddings": 12,
			    "out_channels": 3,
			    "up_block_types": [
			        "AttnUpDecoderBlock2D",
			        "UpDecoderBlock2D",
			    ],
			    "vq_embed_dim": 4,
			}



	@property
	def 	_lowerCamelCase     ( self						):
			torch.manual_seed(0						)
			lowerCAmelCase  :     int       						= VQModel(**self.dummy_movq_kwargs						)
			return model



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= self.dummy_text_encoder
			lowerCAmelCase  :     Optional[int]       						= self.dummy_tokenizer
			lowerCAmelCase  :     Tuple       						= self.dummy_unet
			lowerCAmelCase  :     List[Any]       						= self.dummy_movq

			lowerCAmelCase  :     Any       						= {
			    "num_train_timesteps": 1_000,
			    "beta_schedule": "linear",
			    "beta_start": 0.00085,
			    "beta_end": 0.012,
			    "clip_sample": False,
			    "set_alpha_to_one": False,
			    "steps_offset": 0,
			    "prediction_type": "epsilon",
			    "thresholding": False,
			}

			lowerCAmelCase  :     int       						= DDIMScheduler(**a_						)

			lowerCAmelCase  :     Any       						= {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "movq": movq,
			}

			return components



	def 	_lowerCamelCase     ( self		,   a_		,   a_=0						):
			lowerCAmelCase  :     Any       						= floats_tensor((1, self.cross_attention_dim)		,   rng=random.Random(a_						)						).to(a_						)
			lowerCAmelCase  :     Any       						= floats_tensor((1, self.cross_attention_dim)		,   rng=random.Random(seed + 1						)						).to(a_						)
			# create init_image
			lowerCAmelCase  :     List[str]       						= floats_tensor((1, 3, 64, 64)		,   rng=random.Random(a_						)						).to(a_						)
			lowerCAmelCase  :     Any       						= image.cpu().permute(0		,   2		,   3		,   1						)[0]
			lowerCAmelCase  :     List[str]       						= Image.fromarray(np.uinta(a_						)						).convert("RGB"						).resize((256, 256)						)

			if str(a_						).startswith("mps"						):
					lowerCAmelCase  :     Optional[int]       						= torch.manual_seed(a_						)
			else:
					lowerCAmelCase  :     str       						= torch.Generator(device=a_						).manual_seed(a_						)
			lowerCAmelCase  :     List[str]       						= {
			    "prompt": "horse",
			    "image": init_image,
			    "image_embeds": image_embeds,
			    "negative_image_embeds": negative_image_embeds,
			    "generator": generator,
			    "height": 64,
			    "width": 64,
			    "num_inference_steps": 10,
			    "guidance_scale": 7.0,
			    "strength": 0.2,
			    "output_type": "np",
			}
			return inputs





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= "cpu"

			lowerCAmelCase  :     Optional[Any]       						= self.get_dummy_components()

			lowerCAmelCase  :     Dict       						= self.pipeline_class(**a_						)
			lowerCAmelCase  :     Any       						= pipe.to(a_						)

			pipe.set_progress_bar_config(disable=a_						)

			lowerCAmelCase  :     Union[str, Any]       						= pipe(**self.get_dummy_inputs(a_						)						)
			lowerCAmelCase  :     int       						= output.images

			lowerCAmelCase  :     Tuple       						= pipe(
			    **self.get_dummy_inputs(a_						)		,   return_dict=a_		,   )[0]

			lowerCAmelCase  :     Optional[Any]       						= image[0, -3:, -3:, -1]
			lowerCAmelCase  :     Union[str, Any]       						= image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)

			lowerCAmelCase  :     Dict       						= np.array(
			    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]						)
			assert (
			    np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
			), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
			assert (
			    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
			), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 		lowerCamelCase	(   unittest.TestCase							):



	def 	_lowerCamelCase     ( self						):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= load_numpy(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
			    "/kandinsky/kandinsky_img2img_frog.npy"						)

			lowerCAmelCase  :     List[Any]       						= load_image(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"						)
			lowerCAmelCase  :     List[str]       						= "A red cartoon frog, 4k"

			lowerCAmelCase  :     Any       						= KandinskyPriorPipeline.from_pretrained(
			    "kandinsky-community/kandinsky-2-1-prior"		,   torch_dtype=torch.floataa						)
			pipe_prior.to(a_						)

			lowerCAmelCase  :     Dict       						= KandinskyImgaImgPipeline.from_pretrained(
			    "kandinsky-community/kandinsky-2-1"		,   torch_dtype=torch.floataa						)
			lowerCAmelCase  :     Any       						= pipeline.to(a_						)

			pipeline.set_progress_bar_config(disable=a_						)

			lowerCAmelCase  :     Optional[int]       						= torch.Generator(device="cpu"						).manual_seed(0						)
			lowerCAmelCase						,		lowerCAmelCase  :     int       						= pipe_prior(
			    a_		,   generator=a_		,   num_inference_steps=5		,   negative_prompt=""		,   ).to_tuple()

			lowerCAmelCase  :     Tuple       						= pipeline(
			    a_		,   image=a_		,   image_embeds=a_		,   negative_image_embeds=a_		,   generator=a_		,   num_inference_steps=100		,   height=768		,   width=768		,   strength=0.2		,   output_type="np"		,   )

			lowerCAmelCase  :     Optional[Any]       						= output.images[0]

			assert image.shape == (768, 768, 3)

			assert_mean_pixel_difference(a_		,   a_						)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   list[tuple[int, int]]

lowerCAmelCase									=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase									=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     int       						= pos_x
			lowerCAmelCase  :     Union[str, Any]       						= pos_y
			lowerCAmelCase  :     Optional[Any]       						= (pos_y, pos_x)
			lowerCAmelCase  :     Union[str, Any]       						= goal_x
			lowerCAmelCase  :     Tuple       						= goal_y
			lowerCAmelCase  :     str       						= g_cost
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     List[Any]       						= self.calculate_heuristic()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= abs(self.pos_x - self.goal_x						)
			lowerCAmelCase  :     Any       						= abs(self.pos_y - self.goal_y						)
			return dx + dy





	def __lt__( self		,   a_						):
			return self.f_cost < other.f_cost






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= Node(start[1]		,   start[0]		,   goal[1]		,   goal[0]		,   0		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= Node(goal[1]		,   goal[0]		,   goal[1]		,   goal[0]		,   99_999		,   a_						)

			lowerCAmelCase  :     Tuple       						= [self.start]
			lowerCAmelCase  :     list[Node]       						= []

			lowerCAmelCase  :     Tuple       						= False



	def 	_lowerCamelCase     ( self						):
			while self.open_nodes:
					# Open Nodes are sorted using __lt__
					self.open_nodes.sort()
					lowerCAmelCase  :     List[Any]       						= self.open_nodes.pop(0						)

					if current_node.pos == self.target.pos:
							lowerCAmelCase  :     Tuple       						= True
							return self.retrace_path(a_						)

					self.closed_nodes.append(a_						)
					lowerCAmelCase  :     Tuple       						= self.get_successors(a_						)

					for child_node in successors:
							if child_node in self.closed_nodes:
									continue

							if child_node not in self.open_nodes:
									self.open_nodes.append(a_						)
							else:
									# retrieve the best current path
									lowerCAmelCase  :     int       						= self.open_nodes.pop(self.open_nodes.index(a_						)						)

									if child_node.g_cost < better_node.g_cost:
											self.open_nodes.append(a_						)
									else:
											self.open_nodes.append(a_						)

			if not self.reached:
					return [self.start.pos]
			return None



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= []
			for action in delta:
					lowerCAmelCase  :     Dict       						= parent.pos_x + action[1]
					lowerCAmelCase  :     str       						= parent.pos_y + action[0]

					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(a_						) - 1):
							continue

					if grid[pos_y][pos_x] != 0:
							continue

					successors.append(
					    Node(
					        a_		,   a_		,   self.target.pos_y		,   self.target.pos_x		,   parent.g_cost + 1		,   a_		,   )						)
			return successors





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= node
			lowerCAmelCase  :     List[str]       						= []
			while current_node is not None:
					path.append((current_node.pos_y, current_node.pos_x)						)
					lowerCAmelCase  :     Dict       						= current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
					lowerCAmelCase									=   (0, 0)
					lowerCAmelCase									=   (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("""------""")

					lowerCAmelCase									=   GreedyBestFirst(init, goal)
					lowerCAmelCase									=   greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															lowerCAmelCase									=   2

										for elem in grid:
															print(elem)



